Once a network, such as an optical network, is deployed, the maintenance of service availability according to Service Level Agreements (SLAs) necessitates the storage of spare parts at the ready to service deployed parts following faults. However, for large and/or complex networks, the selection of the exact quantities of spares to store at particular depots can be complicated, especially for networks serviced by multiple depots.
Further, the assessment of the risk and cost associated with various spares configurations (which is important to align the selection of spares quantities with business objectives and strategies) is currently not available using a single tool. Thus, unreliable estimates and guesswork are required and routinely utilized. However, once risk levels are objectively quantified, informed judgments can be made concerning the trade-offs between the risk of spares depletion and the cost of mitigation.
Additionally, the task of assuring the correct levels of spares servicing a dynamic network is human-resource intensive and involves multiple manual procedures. Particularly, the monitoring of a network for changes in the requirements for spares is not presently automated.
The state-of-the-art procedure for determining the number of spares to place at each depot is manual and involves the selection of type and quantity of spares to place on an ad-hoc or ‘rule of thumb’ basis. The handling of placing spares for large and/or complex networks is carried out by an individual who may typically spend several weeks acquiring data and still, in the end, resort to guesswork in order to bolster spares holdings. Spares are normally purchased at the time of network build (for example, 3 spares for every 30 parts). However, the network can rapidly change and what may have previously been 30 spares for parts (SFPs) can quickly become more than 300 SFPs within the space of a year. The many individuals involved in the various aspects of providing spares for the network do not usually have a central repository to store data or new pieces of information, and so related information is usually multiple and in generally unknown locations.
Referring specifically to FIG. 1, the state-of-the-art procedure 10 for determining the number of spares to place at each depot typically involves tech support 12 obtaining network data and network inventory from a Network Management System (NMS) 14, service operations 16 retrieving spares inventory from a Logistics Management Tool (LMT), and an account manager 20 retrieving contractual obligations 22, all of which can take from days to months. This information is then passed to a systems engineer 24, who determines Network Element (NE) locations 26, maps the NEs to specific spares depots 28, creates a spares map algorithm 30, and determines the required spares per location per part and determines the present additional parts to order 32, all of which can take weeks. The result is a spares analysis report 34 which is used by service operations 36 to set the required spares, which information is again passed to the LMT 38.
The shortcomings of this procedure include the following: there are no procedures in place to know whether SLAs are being kept; there is no way of knowing the actual coverage levels for networks served by multiple depots, therefore estimates are needed; there are too many manual or labor-intensive tasks; due to the durations and resources involved in completing a spares analysis, the process is completed annually and there could be extended periods of non-coverage following network upgrades; interdepartmental communication delays lead to increased analysis times; large manpower is required to complete a single analysis; following just one network upgrade, the whole process must be completed in order to know whether or how many more spares are needed; the notification of network changes for the process of spares management requires additional agreements and communications with the customer; tracking the location of each site requires additional and extensive communication with the customer; the management of important information, such as SLA requirements, spares algorithms, and the mapped location of NEs is difficult; and there are potential levels of spares coverage inconsistencies within large and/or complex networks serviced by multiple depots, as different systems engineers (in charge of different regions) may employ different algorithms.
Thus, the state-of-the-art procedure for the monitoring and recommendation of spares requirements is largely manual and especially difficult for large and/or complex networks, where recommendations are the result of rough estimates and best guesses. Moreover the risk associated with particular levels of spares is not currently quantified or even known, which does not facilitate objective business decision making regarding risk versus cost. For complex networks spanning multiple geographies or served by multiple spares depots, the analyses of what type and how many spares to place at particular depots becomes extremely difficult and prone to error. Such work typically spans multiple departments and functions, and is subject to low priority and communication delays. Following network changes, the calculation of the new requirements for network spares does not take place due to the complexity, lack of data, and extreme length of time required. Therefore, shortages in spares holdings are known only when a critical fault (that cannot be serviced by an available spare part) occurs; which can cost the party responsible thousands of dollars in SLA breaches. Further, over a period of several years, the tracking of contractual obligations or special arrangements can become difficult, as the original sales leads, negotiators, and/or systems engineer analyzers of spares may have moved to a different role or left a vendor.